Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the way businesses operate, automating routine tasks, improving efficiency and enabling real-time decision-making. While some companies are still evaluating the long-term returns on AI investments, the technology has already helped automate repetitive processes, reduce manpower requirements and speed up execution.
In India, banking and financial services, manufacturing, and information technology sectors have embraced AI at scale. The railway sector, however, has been slower to adopt these technologies despite the government's push to modernise ageing infrastructure. Although Indian Railways has begun deploying technologies such as the Automatic Train Protection system, much of its vast network still relies heavily on manual inspection and maintenance.
Bengaluru-based e2E Rail's subsidiary Nova Control Technologix has partnered with Tata Elxsi to integrate AI into railway signalling systems, train coaches and other critical equipment. The objective is to develop predictive systems capable of identifying potential failures or safety risks before they occur.
“Historically, railways have largely been reactive when it comes to maintenance. As railway systems evolve and there is a need for faster, safer and more efficient operations, we are entering an era where systems must continuously monitor themselves and predict failures before they happen. That is the fundamental idea behind integrating AI into railway infrastructure," said Sourajit Mukherjee, director & CEO, Nova Control Technologix, and CEO of e2E Rail.
Nova Control Technologix plans to invest around ₹100 crore over the next two to three years to develop advanced railway signalling systems and AI-enabled technologies for train coaches. Tata Elxsi will provide expertise in cybersecurity and data protection to build secure rail safety networks.
The companies are also developing digital twin technology to simulate and validate AI models before they are deployed across Indian Railways' 1.32 lakh-km network.
“The combination of AI analytics and digital twins offers several advantages. It allows operators to detect anomalies early, predict asset degradation and reduce downtime, directly improving punctuality and operational efficiency. Higher asset availability makes railway operations more reliable and ultimately enhances the passenger experience," Mukherjee said.
According to Jayaraj Rajapandian, head of aerospace, rail and off-highway at Tata Elxsi, AI has been embedded into the system architecture from the design stage itself. "From the very beginning, while architecting the product, we ensured that an AI engine was an integral part of the design. We deliberately built an architecture that is modular, scalable and expandable, while also allowing flexibility to scale down wherever required. This enables us to address a wide range of use cases and future requirements. From both safety and predictive maintenance perspectives, the architecture allows us to deploy advanced algorithms that support decision-making and ensure safer and more efficient railway operations," he said.
Rajapandian added that Tata Elxsi and e2E Rail are working on a long-term technology roadmap to develop solutions that can evolve with the changing needs of the railway sector.
Industry estimates suggest that Indian Railways spends nearly 65-70% of its maintenance budget on existing assets. Experts believe wider adoption of AI-driven predictive maintenance could reduce this to below 50%. Indian Railways currently spends around ₹1 lakh crore annually on safety and maintenance of its infrastructure.
"Indian Railways deploys a massive workforce for maintenance because of the sheer scale of its operations. AI has the potential to fundamentally change this by making systems self-diagnostic, self-predictive and eventually more autonomous. By leveraging AI and ML, these systems can identify patterns, predict failures before they occur and recommend corrective action," Mukherjee said.